MERC uses artificial intelligence to turn big data into actionable information using cutting-edge machine learning frameworks such as TensorFlow, Keras, and sci-kit learn to enhance engineering solutions. MERC develops data engineering pipelines that include preprocessing and filtering data, data normalization, and feature engineering before ultimately training and deploying machine learning models to make sense of complex data architectures.
MERC recognizes that different problems in big data each require unique solutions tailor-made for that specific problem. That’s why our engineers are equipped with a diverse set of algorithms to tackle the problem. Whether working with time series data, digital signal processing, audio, visual, or any other data, MERC engineers work with domain experts to determine the best way to approach any problem.
MERC utilizes both deep learning and traditional AI techniques to train models for a variety of classification, regression, and clustering problems. Our experience with AI includes anomaly event detection, identifying damaging flight patterns, object recognition/localization, acoustic sound classification, gesture recognition, event clustering, and regression training. Artificial intelligence can be applied to a variety of problems. As systems generate more data than ever, AI is necessary to make use of the resulting large volumes of data, saving engineers, analysts, and project managers hours of valuable time.